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May 3, 2019
New image added: CUDA 10.1
PyTorch upgraded to 1.1.0
fastai upgraded to 1.0.52
MXNet upgraded to 1.4.0 (and now based on CUDA 10.0 images)
Chainer upgraded to 5.4.0
Apr 26, 2019
We now support two authorization modes in the new release: single user mode and service account mode3.
rpy2 is now pre-installed in the R image.
A plugin for editing metadata of cells is now pre-installed.
jupyterlab-celltags Jupyter Lab extension is now pre-installed.
Fixed bug with sudo (now you can use sudo from the Jupyter Lab terminal).
Downloading files from Jupyter Lab file browser is now working.
Mar 15, 2019
Tensorflow upgraded to version 1.13.
Fairing now preinstalled.
cookiecutter and seaborn now preinstalled.
More descriptive serial logs to help customers debug common issues.
Misc bug fixes.
Due to incompatibilities between Tensorflow 1.13 (which requires Numpy 1.16.2 or greater) and the latest Intel optimized version of Numpy (which is 1.15) we are not using the intel optimized versions of Numpy and Scipy for this release.
Feb 21, 2019
TensorFlow and Pytorch GPU images switch between CPU-only/GPU-enabled binaries at startup depending on whether GPUs are attached.
SSH is not disabled during NVIDIA driver installation on GPU images.
Due to incompatibilities between the latest kernel update (Debian 9.8) and Docker, we have put a hold on the kernel updates for this release (that is,
apt-mark hold linux-image-4.9.0-8-amd64). If you require the latest kernel, you can run
sudo apt-mark unhold linux-image-4.9.0-8-amd64 && sudo apt upgrade, but we cannot guarantee that Docker or our direct JupyterLab link from Marketplace will function correctly if you force the upgrade.
Jan 29, 2019
New TensorFlow 2.0 (experimental) flavor is added.
New experimental ability to use Deep Learning VMs with special Web proxy, instead of SSHing to the VM.
Jan 14, 2019
New MXNet 1.3 (experimental) flavor is added.
December 19, 2018
Launched the new 1.0 version of AI Platform Deep Learning VM Image.
December 19, 2018
BigQuery magic plugin now preloaded all the time.
Jupyter SQL integration now pre-installed and SQL plugin now preloaded.
TensorFlow images now include bazel pre-installed.
Python Dataproc client now pre-installed on all our images.
fastai updated to the latest version 1.0.38.
December 10, 2018
Fixed bug that was resulting in a broken Git UI in some cases.
Fast.Ai updated to 1.0.36.
December 5, 2018
Integrates fix for speed regression in linear models when using TensorFlow with Intel® MKL DNN.
Adds Git-Jupyter integration.
November 20, 2018
Chainer is now upgraded to 5.0.0 (and CuPy to 5.0.0).
CuDNN updated to 7.4.
TensorRT5 updated to GA.
XGBoost updated to 0.81.
Images now have papermill pre-installed.
Ability to change Jupyter UI that is running on the port 8080, currently supported: Lab and Notebook.
November 13, 2018
Fixed an issue where users were locked out of `apt` after startup due to a
package needing configuration. If you are using an M11 image and are
experiencing issues with apt, please either recreate your VM or run
sudo dpkg --configure -a to clear the lock.
November 8, 2018
All GPU images install NVIDIA driver 410.72.
TensorFlow updated to v1.12.0.
PyTorch 0.4 image now uses conda for package management.
October 23, 2018
PyTorch 1.0 updated to the latest build as of October 23.
fastai updated to 1.0.12.
fastai course materials are now available at
Chainer UI updated to 0.6.0.
Chainer MN updated to 1.3.1.
Fixed a bug that was causing Intel packages to be overwritten.
October 10, 2018
Intel Optimized Python packages are installed in all distributions:
- TensorFlow (when applicable)
Chainer updated from v4.4.0 to v4.5.0.
September 27, 2018
New XGBoost images:
New CUDA 10.0 image (common-cu100) with the following NVIDIA stack in it:
- CuDNN 7.3
- NCCL 2.3.4
- Driver 410.48
- TensorRT 5
TensorFlow updated from v1.10.1 to v1.11.0.
TensorFlow now compiled with CUDA 10.0 and CuDNN 7.3.
Common CUDA 9.2 image now has latest NCCL 2.3.4
Common CUDA 9.0 image now has:
- latest NCCL 2.3.4
- latest CuDNN 7.3
- TensorRT 5.0.0
Following packages are now pre-installed on the images:
After SSHing to the instance you now will see the exact revision of the image in the header.
September 18, 2018
Introducing new experimental images with PyTorch 1.0RC. New image families are:
September 12, 2018
Chainer updated from v4.3.0 to v4.4.0.
Better integration with BigQuery.
Pillow has been replaced with the faster Pillow-SIMD package.
minikube is now pre-installed.
New simplified image families introduced:
Jupyter now running on behalf of its own user (not root).
August 30, 2018
Introducing experimental images: these images bring new frameworks for you to try out, but they come with no guarantees of future support. Current experimental images:
- Chainer (4.3)
All images now have
TensorFlow updated from v1.10.0 to v1.10.1.
August 14, 2018
All images now have Docker and/or NVIDIA Docker pre-installed.
TensorFlow and PyTorch images now include pre-baked tutorials.
GPU flavors of TensorFlow and PyTorch images now swap binaries to the CPU optimized binaries during the first boot if the instance does not have a GPU.
July 31, 2018
Includes Tensorfow Serving: model server binary at /usr/local/bin/tensorflow_model_server and tensorflow-serving-api preinstalled.
Integration with Colab: default jupyterlab instance can be connected as a Colab backend.
Upgraded to support CUDA 9.2 (note this changes the pytorch family name).
Fixed an issue with CUDA linking in the build process, binaries up to 10% faster now.
July 17, 2018
New common image with CUDA 9.0 has been introduced.
The following changes are included in this release:
Bug that was preventing Jupyter Notebook from working correctly has been resolved.
July 11, 2018
TensorFlow updated to version 1.9.0.
New public Google Group for users: google-dl-platform
July 2, 2018
AI Platform Deep Learning VM Image is available as a beta release.